Self-selection and non-response biases in customers’ hotel ratings – a comparison of online and offline ratings

Smironva, Ekaterina; Kiatkawsin, Kiattipoom; Lee, Seul Ki; Kim, Jinhoo; Chung-Hun, Lee

doi:10.1080/13683500.2019.1599828

Cited by 30 publications

(23 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the process of investigation, there are many factors that lead to the missing or non-response of some individuals in the sample, so the final valid sample is only a part of the survey sample (Smironva et al, 2019 ). Therefore, formal experiments also focus on solving the problem of non-response bias.…”

Section: Methodsmentioning

confidence: 99%

The Impacts of Perceived Risk and Negative Emotions on the Service Recovery Effect for Online Travel Agencies: The Moderating Role of Corporate Reputation

Wei

2021

Front. Psychol.

View full text Add to dashboard Cite

This study explores the impact mechanism of perceived risk and negative emotions on the service recovery effect of an online travel agency (OTA) through a scenario experiment. The results show that: perceived risk has positive and negative impacts on negative emotions and service recovery satisfaction, negative emotions have a negative impact on service recovery satisfaction, and corporate reputation plays a positive moderating role in the relationship between perceived risk and service recovery satisfaction. This study is helpful to better explain the impact mechanism of the service recovery effect of OTAs, and to provide a theoretical reference for improving the service recovery effect of OTAs.

show abstract

Section: Methodsmentioning

confidence: 99%

The Impacts of Perceived Risk and Negative Emotions on the Service Recovery Effect for Online Travel Agencies: The Moderating Role of Corporate Reputation

Wei

2021

Front. Psychol.

View full text Add to dashboard Cite

show abstract

“…In addition, when they express their opinion, customers are not only influenced by their tourist experience but also by their experience using the online platform where they booked and wrote their review [4]. In any case, most online hotel reviews show very positive average ratings [31], especially in the area of accommodation which is part of the sharing economy where it is common to have a direct contact between the host and the client, which can affect the latter's decision to express negative opinions [13].…”

Section: Online Reviews/scoring and Customer Satisfactionmentioning

confidence: 99%

A Client-Centric Evaluation System to Evaluate Guest’s Satisfaction on Airbnb Using Machine Learning and NLP

Chiny

Bencharef

Hadi

et al. 2021

Applied Computational Intelligence and Soft Computing

View full text Add to dashboard Cite

Understanding the determinants of satisfaction in P2P hosting is crucial, especially with the emergence of platforms such as Airbnb, which has become the largest platform for short-term rental accommodation. Although many studies have been carried out in this direction, there are still gaps to be filled, particularly with regard to the apprehension of customers taking into account their category. In this study, we took a machine learning-based approach to examine 100,000 customer reviews left on the Airbnb platform to identify different dimensions that shape customer satisfaction according to each category studied (individuals, couples, and families). However, the data collected do not give any information on the category to which the customer belongs to. So, we applied natural language processing (NLP) algorithms to the reviews in order to find clues that could help us segment them, and then we trained two regression models, multiple linear regression and support vector regression, in order to calculate the coefficients acting on each of the 6 elementary scores (precision, cleanliness, check-in, communication, location, and value) noted on Airbnb, taking into account the category of customers who evaluated the performance of their accommodation. The results suggest that customers are not equally interested in satisfaction metrics. In addition, disparities were noted for the same indicator depending on the category to which the client belongs to. In light of these results, we suggest that improvements be made to the rating system adopted by Airbnb to make it suitable for each category to which the client belongs to.

show abstract

“…Hence, the pressure to write reviews after staying is higher compared to staying at hotels [35]. Unlike numeric ratings, where the scores can be easily understood and compared, albeit subjected to the various amount of biases [15], text reviews offer a richer and deeper set of information. Text reviews are usually presented in an unstructured manner creating the inherent issues of (1) making it challenging to locate relevant information, also known as uncertainty, and (2) creating confusion when there is conflicting information, or equivocality [25].…”

Section: Online Text Reviewsmentioning

confidence: 99%

“…However, this approach only considers words in isolation. Moreover, numeric ratings have already been used to determine the overall sentiment and satisfaction [15]. Hence, a new family of automated text analysis based on word co-occurrences was adopted in order to determine the topics of discussion in text corpora.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Automated Text Analysis of Airbnb Reviews in Hong Kong and Singapore Using Latent Dirichlet Allocation

Kiatkawsin

Sutherland

Kim³

2020

Sustainability

Self Cite

View full text Add to dashboard Cite

Airbnb has emerged as a platform where unique accommodation options can be found. Due to the uniqueness of each accommodation unit and host combination, each listing offers a one-of-a-kind experience. As consumers increasingly rely on text reviews of other customers, managers are also increasingly gaining insight from customer reviews. Thus, this present study aimed to extract those insights from reviews using latent Dirichlet allocation, an unsupervised type of topic modeling that extracts latent discussion topics from text data. Findings of Hong Kong’s 185,695 and Singapore’s 93,571 Airbnb reviews, two long-term rival destinations, were compared. Hong Kong produced 12 total topics that can be categorized into four distinct groups whereas Singapore’s optimal number of topics was only five. Topics produced from both destinations covered the same range of attributes, but Hong Kong’s 12 topics provide a greater degree of precision to formulate managerial recommendations. While many topics are similar to established hotel attributes, topics related to the host and listing management are unique to the Airbnb experience. The findings also revealed keywords used when evaluating the experience that provide more insight beyond typical numeric ratings.

show abstract

Self-selection and non-response biases in customers’ hotel ratings – a comparison of online and offline ratings

Cited by 30 publications

References 48 publications

The Impacts of Perceived Risk and Negative Emotions on the Service Recovery Effect for Online Travel Agencies: The Moderating Role of Corporate Reputation

The Impacts of Perceived Risk and Negative Emotions on the Service Recovery Effect for Online Travel Agencies: The Moderating Role of Corporate Reputation

A Client-Centric Evaluation System to Evaluate Guest’s Satisfaction on Airbnb Using Machine Learning and NLP

A Comparative Automated Text Analysis of Airbnb Reviews in Hong Kong and Singapore Using Latent Dirichlet Allocation

Contact Info

Product

Resources

About